Answer:
a. non response bias
This is one of the usually cause when we use a non-random sample, sinc the probability of selection for all the individuals on the population is not the same when we use this, we are comitting non response bias since we are not taking in count some people in the possible target sample.
Nonresponse bias is "the bias that results when respondents differ in meaningful ways from nonrespondents. Nonresponse is often problem with mail surveys, where the response rate can be very low".
Step-by-step explanation:
Random sample
For this method we need the following two conditions:
(1) "Every element in our population has a nonzero probability of being selected as part of the sample."
(2)" We have accurate knowledge of this probability, known as the inclusion probability, for each element in the sampling frame".
Non random sample
It's the opposite of random sample and we have these problems associated:
(1) "It is relatively unusual to have a sampling frame available to you when you’re conducting market studies".
(2) "Ensuring that every individual in a population has a nonzero probability of being selected is just as difficult to accomplish; knowing every sampling unit’s exact inclusion probability is even more difficult. The individuals that cannot be selected as part of a sample are generally referred to as excluded units".
Assuming the following options:
a. non response bias
This is one of the usually cause when we use a non-random sample, sinc the probability of selection for all the individuals on the population is not the same when we use this, we are comitting non response bias since we are not taking in count some people in the possible target sample.
Nonresponse bias is "the bias that results when respondents differ in meaningful ways from nonrespondents. Nonresponse is often problem with mail surveys, where the response rate can be very low".
b. parameter
False we are looking for a cause related to non random sample. The parameter is just a value that we want to find but is not a cause related to the non random sample.
c. statistics
False, we can associate a cause of non random sample with the statistics. The term "statistics" is a big concept that involves a lot of methods and ways to analyze information, and is not the correct cause associated to the non-random sample.
d. population
False, we can associate the population as a cause of the non random sample. We use sampling methods in order to estimate some population parameters. But the population is not a cause of the non-random sample.
